Bibliographic record
Abstract
The current study explored factors influencing career choices of Asian American social workers and assessed if their personal characteristics and career-related experiences affected their perceived glass ceiling, perception of ethnic discrimination, and perception of career prospects. A total of 208 Asian American social work administrators, supervisors, practitioners and graduate social work students participated in a comprehensive online survey. Participants provided basic demographic and career-related information and completed a set of measures to explore their reasons of choosing social work as their career, and their career perceptions and prospects. Correlation analyses and multiple regression analyses were used to identify predictors of their perception of ethnic discrimination, perceived glass ceiling, and perception of career prospects. The findings showed that altruistic reasons were very important to extremely important in their choice of social work as their career. Social work idealism was found to associate positively with both altruistic reasons and professional concerns of choosing social work. There was a significant relationship between participant’s immigration status and family influence on their career choice. Those who were not born in the U.S. were more likely to be influenced by their family in their career choice than those who were born in in the U.S. Those whose parents were not born in the U.S. were more likely to be influenced by their family expectations on their career choice than either of their parents was born in the U.S. Perceptions of organizational fairness was found to be a strong predictor of perceived glass ceiling, perception of ethnic discrimination, and perception of career prospects. Implications of the findings for social work education and future research were discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".